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Technology has always been the fundamental driver of improvement for human quality of life. And with the recent rollout of advanced frontier models, like OpenAI’s GPT-6, humanity can now massively accelerate scientific discovery for its benefit. Sarah Guo and Elad Gil are joined by OpenAI President and co-founder Greg Brockman to discuss the company’s rapid progress across frontier models, cybersecurity, and scientific discovery. Greg shares how nearly 400 century-old math problems have been solved using its AI agents, and how the technology has the potential to transform other fields like biology in the realm of drug discovery. He also talks about how proactive, always-on AI agents like Dots impact modern productivity, why he believes the best UI is no UI at all, how cybersecurity is in a critical window of time to use peak intelligence to find and fix vulnerabilities in systems, and why OpenAI remains committed to keeping humans in the driver’s seat even as models surpass us in capability.
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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @gdb | @OpenAI
Chapters:
00:42 – Trailer
00:59 – Greg Brockman Introduction
01:11 – AI and Advancements in Mathematics
05:55 – AI Communication and Oversight
07:45 – AI for Overcoming Biology Data Bottlenecks
11:28 – Revolutionizing Drug Discovery
14:18 – Changing Regulations to Accelerate Progress
16:51 – Dots the Proactive Agent
19:19 – Unifying Work and Consumer Agents
22:02 – How UI Will Evolve
26:08 – Cybersecurity in the AI Age
32:55 – Shift in Security Investment
35:49 – OpenAI’s Human-Centric Ethos
38:28 – Conclusion
Is the future of frontier AI open or closed? ReflectionAI co-founder and CEO Misha Laskin joins Sarah Guo and Elad Gil to discuss the launch of Beam, the company’s 500 billion parameter open-weight reasoning model. Misha breaks down the pre-training and reinforcement learning required to produce Beam’s reasoning efficiency, the shift in enterprise compute from renting to owning intelligence, and how he believes that open models will capture the majority of global token demand. He also talks about the open model ecosystem in China and why competition with China’s models is a good thing, safety considerations around open models, and how frontier-level open models may accelerate the pace of scientific discovery.
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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @MishaLaskin | @reflection_ai
Chapters:
00:42 – Misha Laskin Introduction
00:59 – Latest with ReflectionAI
02:07 – Challenges Building an Open Model
03:56 – ReflectionAI’s Agentic Shift
07:37 – Resources for Model Training
10:09 – Scaling Efficiency
12:18 – Training Beam
16:51 – Where Model Value Comes From
19:55 – Beam’s Reasoning Efficiency
22:35 – Monetizing Open Weight Models
25:03 – Future of Open Versus Closed Tokens
31:29 – Competition with Open Models
34:38 – Chinese Open Source Model Ecosystem
38:40 – Will Chinese Models Remain Open
44:44 – Safety and Open Models
53:02 – Debating Access to Powerful Tools
57:16 – AI and Scientific Progress
01:00:36 – Data Centers and Jobs
01:02:07 – Beam and Scientific Research
01:05:37 – Research Head Count to Compute Ratio
01:10:29 – Conclusion
Founder and CEO of full-stack AI chip company Fractile, Walter Goodwin, joins Sarah Guo to discuss the bets he’s made on the future of the chip market as other major players like Broadcom, NVIDIA, and AMD try to accelerate their workloads. They discuss the difference in Fractile’s newer approach on model architecture with a full-stack team in the current chip landscape and the technical bets they’re making in that direction. Walter also talks about compressing the gap between the chip design cycle and its payoff period, and making a generational leap in AI inference to realize the bet in volume against the value to be captured.
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Chapters:
00:46 – Walter Goodwin and Fractile Introduction
02:29 – The Chip Landscape Now
04:56 – Common Handoffs From Architecture-Focused Players
07:18 – Full Stack Approach and Team Setup
09:49 – Fractile’s Most Important Technical Bets
15:32 – Workload Predictions and Compressing the Chip Design Cycle
23:03 – Architect Intent to Output Bottlenecks and Accelerating Trials
28:16 – Workload Bets on Model Architectural Shifts
31:20 – The Future of AI Chip Players and Market Structure
35:14 – Conclusion
Can AI transform legacy incumbents rather than replacing them? Sequence Holdings co-founder and CEO Michael Lee joins Sarah Guo to discuss how holding company structures and engineering integrations are reshaping market leaders from the inside out. Michael details Sequence’s $7.7 billion take-private transaction of Baldwin alongside Dell Family Office (DFO), and shares his thesis on why traditional consulting models and software sales fall short for real enterprise AI transformations. They also talk about why permanent holding company structures are good for long-term compounding, real-world results from applying frontier engineering to BankSouth, and Michael’s lessons from his time in public investing, private equity, and operating at the intersection of market incumbents and AI.
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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @mjlee_2014 | @seqholdings
Chapters:
00:34 – Michael Lee Introduction
01:03 – Sequence Holdings and Baldwin
01:54 – Idea for Sequence
04:36 – Incumbents in the AI Era
06:32 – Why a Holding Company
11:21 – Recruiting Top Engineers
13:08 – Investing in BankSouth
17:46 – Why an Insurance Brokerage
20:17 – Atlas Platform Explained
23:53 – Traditional Private Equity Limitations
27:23 – What Sequence Looks For in Management Teams
31:04 – Accomplishments at BankSouth
34:45 – Founder Lessons
36:08 – Story of Dell Partnership
37:10 – Career and Investment Approach
40:00 – Value of Exceptional People
42:23 – Conclusion
As generative AI hits hardware and latency bottlenecks, Stanford professor, diffusion pioneer, and Inception co-founder and CEO Stefano Ermon is betting on a radical new architecture. Stefano joins Sarah Guo to talk about Inception, and how his team is applying diffusion architecture beyond images and video into discrete text and code generation. Stefano explains the limitations of autoregressive LLMs, as well as why parallel token generation in diffusion models offers superior inference scaling and hardware utilization on standard GPUs. He also shares details about Inception’s Mercury models, real-world voice agent applications, the software stack required to serve diffusion-based models at scale, academia’s role at the frontier of AI innovations, and why the next era of AI competition will be defined by efficiency.
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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @StefanoErmon | @_inception_ai
Chapters:
00:00 – Stefano Ermon Introduction
00:35 – Research Background
02:54 – Starting Inception
05:59 – Why Diffusion Beats Autoregressive
11:10 – Discrete vs. Continuous Modalities
13:19 – Inception Today
16:45 – Where Speed Wins
17:31 – Inception Customer Base
18:49 – Interaction with Hardware Landscape
19:34 – Inception and the Broader Industry
21:41 – Data Compression and Structure
24:45 – Controllability of Diffusion Modeles
27:25 – Emergent Capabilities at Scale
29:02 – Future Workload Split Between Diffusion vs. Traditional
30:03 – Adoption Challenges
31:44 – Hiring and Team Organization
32:50 – Recursive Self Improvement
34:02 – Resource Allocation
35:10 – Impact of Academia
38:13 – Conclusion
From the tokenization of real-world assets to funding research on anti-aging therapeutics, Coinbase co-founder and CEO Brian Armstrong is focused on solving meta-problems for the benefit of society. Brian joins Elad Gil this episode to discuss the intersections of artificial intelligence, crypto rails, and biotechnology. He details Coinbase’s vision for agentic finance, as well as how Coinbase is becoming the ‘Everything Exchange’, which includes tokenized real-world stocks, stablecoin payment adoption, and prediction markets. Brian also introduces his new venture, New Limit, a company founded to tackle longevity through epigenetic reprogramming, and outlines initial therapeutic programs and the roadmap toward clinical trials.
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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @brian_armstrong | @Coinbase
Chapters:
00:00 – Cold Open Trailer
00:45 – Brian Armstrong Introduction
01:13 – Coinbase and the Everything Exchange
05:52 – Agentic Commerce
10:00 – AI and Crypto
10:49 – AI Inside Coinbase
15:39 – Productivity and Company Size
17:26 – The Everything Exchange and Tokenization
20:53 – Prediction Markets
23:15 – Introducing New Limit
34:17 – Looking Forward Five Years
40:24 – Special Economic Zones
44:26 – Conclusion
From data center orchestrators to AGI and robotics, CPUs remain the heart of modern computing. Arm CEO Rene Haas joins Elad Gil and Sarah Guo to explore how Arm is positioned at the epicenter of AI-driven demands for compute. Rene explains Arm’s position in the chip supply chain, and how Arm transitioned from an IP licensing model to producing physical chips like the Arm AGI CPU for Meta. He also discusses bottlenecks in hardware supply chains, SoftBank’s ecosystem and capital strategy, why US semiconductor manufacturing independence is critical, the future of robotics, and why CPUs remain crucial for executing AI workloads.
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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @renehaas237 | @Arm
Chapters:
00:00 – Cold Open Trailer
00:49 – Rene Haas Introduction
01:14 – Arm and Chip Supply Chain
02:37 – Shift from IP to Manufacturing CPUs
04:23 – CPU IP and Customers
06:55 – AI Adoption at Arm
10:15 – Changes in Chip Time to Market
13:27 – Data Center Buildout Bottleneck
15:13 – Softbank Leverage and Capital Strategy
17:43 – Softbank Portfolio Overview
20:13 – Robotics Opportunities for Arm
24:49 – US Manufacturing Protectionism
28:59 – Data Center Backlash
32:30 – Arm Outlook
33:31 – CPU Opportunity
37:06 – Conclusion
Google’s purchase of Spirit Airlines’ data out of bankruptcy signaled a shift in how the tech world values real-world datasets. Although compute and models get much of the attention, in this landscape, it’s data that is a company’s protective moat. Eon CEO / Co-Founder Ofir Ehrlich and President / Co-Founder Gonen Stein join Elad Gil to talk about how Eon is redefining cloud backup into a secure data foundation designed to power and protect enterprise AI. Ofir and Gonen discuss why historical enterprise data is in demand by AI labs, and how Eon facilitates access to scattered and locked data across business units through providing the mapping, classification, and access controls needed to connect it into AI workflows. They also explore how traditional ransomware defenses must now protect against rogue AI agents with legitimate system permissions, concerns around the influx of autonomous agents and non-human identities, and the implications for the breakneck speed of AI adoption compared to the slowness of the cloud era.
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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Eon_io_ | @OfirEhrlich
Chapters:
00:00 – Cold Open Trailer
00:59 – Ofir Ehrlich and Gonen Stein Introduction
01:27 – What Eon Does
02:41 – Data as Moat
06:43 – Training Agents with Good Data
09:39 – Data is the New Oil
15:00 – Autonomous Security Threats
18:15 – How Agents Change the Enterprise Stack
22:11 – Re-imagining Data Infrastructure
27:52 – Cloud vs. AI Era Shift
30:26 – How AI is Changing Companies
34:31 – Conclusion
Max Hodak, co-founder and CEO of Science Corporation, joins Sarah Guo to discuss the future of vision, brain-computer interfaces, and the human experience. Max explains how Science’s PRIMA retinal implant could restore functional vision for people who have lost their sight, and why treating the brain as a computational system could unlock new approaches to medicine.
They explore the broader potential of neural devices, from restoring lost capabilities to expanding human potential, as well as deeper questions around identity, consciousness, and whether the human experience can persist as our biological hardware changes.
Max also shares Science’s long-term vision for reducing the fragility of the human condition by repairing, replacing, and ultimately upgrading parts of ourselves. Finally, he discusses the surprising parallels between AI models and biological brains, and why AI may offer a powerful new lens for understanding intelligence.
Chapters:
00:00 – Cold Open Trailer
01:40 – Max Hodak Introduction
02:00 – Science Corporation Overview and Origin
02:53 – A Revolutionary Solve for Blindness
06:32 – Scope of Timeline and Engineer Cost
09:10 – Clinic Trial Process
09:45 - The Response from Clinicians
12:21 – Broader Biotech Landscape
14:59 – Brain’s Relationship to Senses
17:35 – The Study of Consciousness
19:50 – Investments in Brain Computer Interface
22:10 – Fertile Ways to Study Neuroscience
24:46 – Biotech Expansion for Science Corporation
27:50 – What Success Looks Like in Neuroscience and Tech
29:06 - Goals Within Human Preservation vs. Adaptation
30:25 – Conclusion
In a world of infinite gaming and entertainment possibilities, how does a centuries-old game stay so popular? Chess.com co-founder and CEO Erik Allebest joins Sarah Guo to explain how the evolution of technology has kept people coming back to chess, even when machines can beat us at the game. Erik talks about how the desire to build a MySpace-like community for chess led to the purchase of a domain name from a bankruptcy sale back in 2005, and scaled into a community with 10 million daily active users and 250 million total registered members. He also discusses the growth of the cultural relevance of chess, how investments from private equity firms General Atlantic and CVC helped grow and strengthen their platform, and how Chess.com is leveraging AI both within the business itself and to make a better product for its community.
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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @chesscom | @erikallebest
Chapters:
00:00 – Cold Open Trailer
01:05 – Erik Allebest Introduction
01:48 – Chess.com Today
02:57 – Buying and Scaling Chess.com
06:29 – Competition and Growth
11:52 – Chess and Cultural Relevance
14:32 – Private Equity Investment
19:31 – Playing Games Amid Evolving Tech
25:09 – Tech, Skill Distribution, and Expertise
28:40 – Chess and Cheating
31:20 – What Makes Chess Special
33:17 – Chess.com Future Vision
34:54 – Founder Advice
36:48 – AGI/ASI Predictions
40:02 – AI Investments at Chess.com
42:13 – How AI May Change Product at Chess.com
43:27 – Poker Rating Algorithms
46:07 – Conclusion
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